Identifying a location in a network where noise is generated

ABSTRACT

Configurations and processes for detecting a location at which noise is being generated in a network are disclosed. A device for identifying a location in a network at which noise is being generated may include: an acquiring unit for acquiring amplitude fluctuations in signals transmitted from a first device via a second device in the network; and a detecting unit for detecting a noise-generating device that is transmitting signals containing noise on the basis of the amplitude fluctuations acquired by the acquiring unit.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2013-250005, filed Dec. 3, 2013, which is incorporated herein in itsentirety.

BACKGROUND

Embodiments of the present invention relate to a device, method, and aprogram for identifying a location in a network where noise isgenerated.

When signals are transmitted upstream in a tree-like network such as acable television broadcast (CATV) and a device is generating noise, thedevices upstream from the noise-generating device and other lower-leveldevices connected to the upper-level device experience communicationinterference.

Patent Literature 1 Laid-Open Patent Publication No. 2004-72477

Patent Literature 2 Laid-Open Patent Publication No. 2004-40737

Patent Literature 3 Laid-Open Patent Publication No. 2009-10815

Techniques have been disclosed for identifying locations at which noiseis being generated (see, for example, Patent Documents 1-3). In oneexample of a technique used to identify noise-generating devices,switches are provided through which signals are allowed to pass upstreamto amplifiers in a network or at which the signals are blocked. Whennoise is generated, each of the switches is switched to either thepassing state or the blocking state, and the noise-generating device isidentified by detecting whether or not any noise is present. Therefore,in this technique, switches have to be provided at each branch in thenetwork. Also, the switches must be switched an average of DN/2 times ina network where D is the number of levels and N is the number ofbranches in communication paths. As a result, the configurations andprocesses used to detect the locations at which noise is being generatedin a network can become complicated using these techniques.

SUMMARY

In various embodiments of the present invention, a device foridentifying a location of noise in a network includes an acquiring unitand a detecting unit. The acquiring unit is connected to the network.The network may be a tree-like structure having an upper-level device ata highest level of the network, one or more second devices at one ormore middle levels of the network, and a plurality of first devices at alowest level of the network. The acquiring unit acquires amplitudefluctuations in signals transmitted from the first devices via at leastone second device to the upper-level device. The detecting unit detectsa noise-generating device that is transmitting signals containing noiseon the basis of the amplitude fluctuations in signals acquired by theacquiring unit. Various other embodiments are directed to a method and aprogram for identifying a location of noise in a network.

This summary of the present invention is not intended to enumerate allof the required characteristics of the present invention. The presentinvention may be realized by any combination or sub-combination of thesecharacteristics.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 is a general schematic diagram of a network.

FIG. 2 is a configuration diagram of an identifying device.

FIG. 3 is a flowchart of the main processing in an identifying methodperformed by the identifying device.

FIG. 4 is a flowchart of a noise generation model creating process.

FIG. 5 is a waveform diagram of the amplitudes of the transmissionsignals acquired by the acquiring unit.

FIG. 6 is a waveform diagram of the amplitudes of the transmissionsignals acquired by the acquiring unit.

FIG. 7 is a waveform diagram of the amplitudes of the transmissionsignals acquired by the acquiring unit.

FIG. 8 is a waveform diagram of amplitude fluctuation rates (AR)calculated by a calculating unit arranged in a time series.

FIG. 9 is a waveform diagram of amplitude fluctuation rates (AR)calculated by the calculating unit arranged in a time series.

FIG. 10 is a waveform diagram of amplitude fluctuation rates (AR)calculated by the calculating unit arranged in a time series.

FIG. 11 is a flowchart of a process for detecting the noise generatinglocation.

FIG. 12 shows an example of a hardware configuration for the computer invarious embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

The following is an explanation of embodiments of the present inventionwith reference to particular embodiments. However, the disclosedembodiments do not limit the present invention in the scope of theclaims. Also, all combinations of characteristics explained in theembodiments are not necessarily required in the technical solution ofthe present invention.

FIG. 1 is a general schematic diagram of a network 10. This network 10has a tree-like structure in which nodes branch form route nodes at eachlevel, and terminals are connected to each node. An example of a network10 is a cable television broadcast network. As shown in FIG. 1, thenetwork 10 includes a broadcast facility 12, nodes 14, amplifiers 16,housing complex devices 18, and modems 20. In the network 10, thebroadcast facility 12 is at an upper level, and the modems 20 are at alower level. Therefore, communication from the modems 20 to thebroadcast facility 12 is upstream communication, and communication fromthe broadcast facility 12 to the modems 20 is downstream communication.The broadcast facility 12 is an example of an upper-level device. Thenodes 14, the amplifiers 16 and the housing complex devices 18 aremid-level devices in the network 10, and are examples of mid-leveldevices connected to modems 20 and are examples of second devices. Themodems 20 are examples of lower-level devices connected to thelower-level of the network 10 and are examples of first devices. Thebroadcast facility 12 is connected to the modems 20 via optic fibers orelectrical lines. The nodes 14, the amplifiers 16, and the housingcomplex devices 18 serve as mid-level devices transmitting signals fromthe modems 20 to upper-level devices connected to the upper level.

The broadcast facility 12 is an example of a route node, and is locatedat the highest level of the network 10. The broadcast facility 12 can belocated at a broadcast company which provides content such as televisionprogramming. The broadcast facility 12 transmits downstream signals suchas picture signals containing content. The broadcast facility 12 alsoreceives upstream signals from the modems 20 from nodes 14 viaamplifiers 16 and housing complex devices 18. The broadcast facility 12has an identifying device 50 which is able to identify the location ofdevices generating noise in the network 10.

A node 14 is connected to the broadcast facility 12 and a plurality ofamplifiers 16. The nodes 14 are installed at the municipal level. Thenode 14 can convert optical signals transmitted by the broadcastfacility 12 into electrical signals. The node 14 then transmits theconverted communication signals to amplifiers 16. The node 14 alsoconverts electrical signals transmitted by the modems 20 into opticalsignals. The node 14 then transmits the converted signals to thebroadcast facility 12.

An amplifier 16 connects a node 14 to a plurality of housing complexdevices 18. Amplifiers 16 may be located at many different levels in acommunication path. An amplifier 16 amplifies communication signalsreceived from a node 14, and transmits them to the housing complexdevices 18. An amplifier 16 also amplifies signals received from thehousing complex devices 18, and transmits them to a node 14.

A housing complex device 18 is connected to a node 14 and to a pluralityof modems 20. A housing complex device 18 may be located in eachbuilding or on each floor of a housing complex such as a condominium. Ahousing complex device 18 includes a protector to protect the network 10from abnormal occurrences such as lightning strikes, an amplifier foramplifying communication signals and transmitted signals, and adistributor for distributing the communication signals to each modem 20.A housing complex device 18 transmits communication signals receivedfrom an amplifier 16 to each modem 20. A housing complex device 18 alsotransmits signals received from a modem 20 to an amplifier 16. A housingcomplex device 18 may convert downstream optical signals into electricalsignals or convert upstream electrical signals into optical signals.

A modem 20 is an example of a terminal node, and is located at thelowest level of the network 10. A modem 20 is connected to a housingcomplex device 18. A modem 20 may be located, for example, in eachhousehold of a housing complex. The modem 20 may be a set-top box (STB)connected to a television, a cable modem connected to a personalcomputer and a telephone to enable two-way data communication, a VoIP(Voice over Internet Protocol) with telephone function, or an EmbeddedMultimedia Terminal Adapter (E-MTA) integrated into a cable modem. Whenthe modem 20 is an STB, the modem 20 converts communication signalsreceived from a broadcast facility 12 via a housing complex device 18into picture signals viewable on a television, and outputs the picturesignals to the television. When the modem 20 is a cable modem, the modem20 converts information entered using a personal computer into signalsthat are transmitted to a broadcast facility 12. The modem 20 transmitsidentification signals for identifying itself along with transmittedsignals.

When there is a nearby source of noise, the modem 20 transmits noisefrom the source in addition to transmitted signals. Examples of noisesources include a loose connector in the communication path, a microwaveoven, and an automatic door.

FIG. 2 is a configuration diagram of an identifying device 50. Theidentifying device 50 identifies whether a node 14, an amplifier 16, ahousing complex device 18 or a modem 20 is a source of noise generatedin the network on the basis of amplitude fluctuations in signalstransmitted from a modem 20. An example of an identifying device 50 is acomputer such as a personal computer. As shown in FIG. 2, theidentifying device 50 includes a control unit 60 and a storage unit 58.The control unit 60 is an arithmetic processing device such as a centralprocessing unit (CPU). The control unit 60 functions as an acquiringunit 52, a detecting unit 54 and a model creating unit 56 by reading aprogram stored in the storage unit 58. A portion of the acquiring unit52, detecting unit 54 and model creating unit 56 may be configured fromhardware such as circuits.

The acquiring unit 52 is connected to the network 10 so as to be able toobtain information such as transmitted signals from optic fibers throughwhich transmitted signals pass. The acquiring unit 52 obtains theamplitude fluctuation in the signals transmitted from modems 20 in thenetwork 10 via housing complex devices 18, amplifiers 16 and nodes 14.For example, the acquiring unit 52 receives signals transmitted by amodem 20 from an optic fiber, and calculates the amplitude fluctuationfrom the amplitude values of the transmitted signals. The acquiring unit52 also acquires identification signals from the modem 20 along with thetransmitted signals. The acquiring unit 52 then associates the amplitudefluctuation with the modem 20 on the basis of the identification signalsfrom the modem 20.

The detecting unit 54 is connected to the acquiring unit 52, the modelcreating unit 56 and the storage unit 58 in order to be able to inputand output information. The detecting unit 54 acquires the amplitudefluctuation from the acquiring unit 52. The detecting unit 54 detectswhether or not a modem 20, housing complex device 18, amplifier 16 ornode 14 is transmitting signals containing noise on the basis of theamplitude fluctuation in the transmitted signals acquired by theacquiring unit 52. For example, the detecting unit 54 detects whether ornot noise has occurred at a level lower than a housing complex device18, an amplifier 16 or a node 14 on the basis of the amplitudefluctuations in the transmitting signals passing through housing complexdevices 18, amplifiers 16 and nodes 14. In addition, the detecting unit54 compares the past amplitude fluctuations of a modem 20 acquired fromthe storage unit 58 with the amplitude fluctuations at the time ofdetection acquired from the acquiring unit 52 to detect whether themodem 20 is generating noise. More specifically, the detecting unit 54detects whether a modem 20 is generating noise using an amplitudefluctuation ratio that is the ratio of the amplitude fluctuations at thetime of detection acquired from the acquiring unit 52 to past amplitudefluctuations acquired from the storage unit 58. The detecting unit 54associates the amplitude fluctuation ratio calculated at the time of anoise generation model with the presence or absence of noise, andoutputs the ratio to the model creating unit 56 or the storage unit 58.The detecting unit 54 also outputs the detected location of the noise toan external display device.

The model creating unit 56 is connected to the detecting unit 54 and thestorage unit 58 so as to be able to input and output information. Themodel creating unit 56 creates a noise generation model in order tocalculate the noise event probability, which is the probability thateach modem 20 will generate noise. For example, when a noise generationmodel is created, the model creating unit 56 acquires, from the storageunit 58, the amplitude fluctuation ratio calculated by the detectingunit 54, and creates a noise generation model for a modem 20 using alogistic regression technique incorporating the amplitude fluctuationratio. The model creating unit 56 then outputs the created noisegeneration model to the detecting unit 54 or the storage unit 58.

The storage unit 58 is connected to the detecting unit 54 and the modelcreating unit 56 so as to be able to input and output information. Thestorage unit 58 stores the programs and information needed to detectlocations at which noise is being generated. For example, the storageunit 58 stores past amplitude fluctuations or the means of pastamplitude fluctuations. Here, “past” can mean any time prior to theacquisition of amplitude fluctuations during noise-generating locationdetection or noise generation model creation for a modem 20 using alogistic regression technique.

FIG. 3 is a flowchart of the main processing in the identifying methodperformed by the identifying device 50. In the main processing, theidentifying device 50 first creates a noise generation model (S10) andthen detects the location at which noise is being generated on the basisof the created noise generation model (S12).

FIG. 4 is a flowchart of the noise generation model creating process inStep S10. FIG. 5, FIG. 6, and FIG. 7 are waveform diagrams oftransmission signal amplitudes acquired by the acquiring unit 52. FIG.8, FIG. 9 and FIG. 10 are waveform diagrams in which amplitudefluctuation ratios AR calculated by the detecting unit 54 have beenarranged in time series. The process for creating a noise generationmodel is executed prior to the process for detecting the location atwhich noise is being generated.

In the process for creating a noise generation model, as shown in FIG.4, the acquiring unit 52 first acquires the amplitude fluctuations ofeach modem 20 (S20). For example, the acquiring unit 52 acquires theamplitudes of signals transmitted by each modem 20 along with theidentification information for each modem 20. The amplitudes of thetransmitted signals acquired by the acquiring unit 52 are the waveformsshown from FIG. 5 to FIG. 7. In FIG. 5 through FIG. 7, the horizontalaxis represents the time, and the vertical axis represents the amplitudeof the transmitted signals at the voltage level used by each modem 20 totransmit signals (unit: dB). DT represents the detection time duringwhich the amplitudes are detected in order to create a noise generationmodel.

FIG. 5 is a waveform diagram of the amplitudes of the signalstransmitted by the modems 20 connected to a single housing complexdevice 18. FIG. 6 and FIG. 7 are also waveform diagrams of theamplitudes of the signals transmitted by the modems 20 connected to asingle housing complex device 18. In FIG. 5 and FIG. 6, the waveforms donot include those of the modem 20 generating noise. In FIG. 7, thewaveforms include those of a modem 20 which generated noise during thedetection time DT. After the detection time DT in FIG. 7, the occurrenceof noise is detected and the waveform is repaired by service personnel.Here, the waveform before and after the detection time DT indicated bythe oval in FIG. 7 clearly fluctuates much more wildly than thewaveforms detected during the detection time DT shown in FIG. 5 and FIG.6.

The waveforms shown in FIG. 5 through FIG. 7 are the waveforms of theamplitudes of modems 20 connected to different housing complex devices18 but the same node 14. When noise is generated by a modem 20, thenoise is known to ingress with the signals transmitted by nearby modems20. (This is known as ingress noise.) The ingress of noise is known tobe higher in modems 20 near a modem 20 generating the noise 20.Therefore, as shown in FIG. 7, when a modem 20 generates noise, thesignals transmitted from nearby modems 20 also include noise.

The acquiring unit 52 calculates the amplitude fluctuation σb from theamplitude of the transmitted signals that have been acquired. An exampleof an amplitude fluctuation is the standard deviation of the amplitude.The acquiring unit 52 calculates the amplitude fluctuation σb on thebasis of Equation (1) shown below. The number of amplitude samples N isthe number of samples during the detection time DT.

$\begin{matrix}{{Equation}\mspace{14mu} 1} & \; \\{{\sigma \; b^{\; 2}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {{Xi} - m} \right)^{2}}}} & (1)\end{matrix}$

N: Number of amplitude samplesXi: Amplitude value (dB)m: Arithmetic mean of N amplitudes

The acquiring unit 52 calculates the amplitude arithmetic mean m usingEquation (2) below.

$\begin{matrix}{{Equation}\mspace{14mu} 2} & \; \\{m = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{Xi}}}} & (2)\end{matrix}$

Here, the detection time DT is eight hours, and the number offluctuation samples N is 480. In this case, the acquiring unit 52samples amplitude value Xi every minute during the detection time DT fora total of 480 samples on the basis of the amplitude waveform. Theacquiring unit 52 substitutes the 480 sampled amplitude values Xi inEquation (2) to calculate the arithmetic mean m of the amplitude. Next,the acquiring unit 52 substitutes the calculated arithmetic mean m andthe 480 amplitude values Xi in Equation (1) to calculate the amplitudefluctuation σb. The acquiring unit 52 obtains amplitude fluctuations σbduring the creation of a noise generation model in this way. Theacquiring unit 52 may acquire the amplitude fluctuations σb directlyfrom the broadcasting facility 12 instead of calculating it from theamplitude values Xi. The acquiring unit 52 outputs the acquiredamplitude fluctuations σb to the detecting device 54 along with theidentification signals of the modem 20.

Here, the acquiring unit 52 may calculate the amplitude fluctuations σbseveral times on the basis of amplitude values acquired using thesliding window technique. For example, when the detection time DT iseight hours, the acquiring unit 52 samples the amplitude values Xi fromamplitudes acquired from 0:00 to 8:00, and calculates a first amplitudefluctuation σb. Next, the acquiring unit 52 samples amplitude values Xifrom amplitudes acquired from 1:00 to 9:00, and calculates a secondamplitude fluctuation σb. In this way, the acquiring unit 52 calculateseach amplitude fluctuation σa during a pth and a p+1th detection timewhich overlap for seven hours. In this way, the acquiring unit 52 cancalculate multiple amplitude fluctuations σb on the basis of amplitudesin a short period of time.

The detecting unit 54 calculates the amplitude fluctuation ratio AR ofeach modem 20 (S22). The amplitude fluctuation ratio AR is the ratio ofthe amplitude fluctuation σb during creation of a noise generation modelto a past amplitude fluctuation σa. Here, “past amplitude fluctuation”includes both past amplitude fluctuations and the mean of past amplitudefluctuations. When calculating an amplitude fluctuation ratio AR, thedetecting unit 54 acquires a past amplitude fluctuation σa stored in thestorage unit 58.

The method used to calculate the past amplitude fluctuations σa storedin the storage unit 58 is the same method used to calculate theamplitude fluctuations σb during the creation of a noise generationmodel described above. Therefore, the acquiring unit 52 calculates andacquires past amplitude fluctuations σa on the basis of the amplitudefluctuation σb calculating and acquiring method described above, andstores them in the storage unit 58. However, the acquiring unit 52 mayuse a detection time DT for creating the noise generation model thatdiffers from the detection time DT used to calculate the past amplitudefluctuation σa. The acquiring unit 52 may also use a number of samplesto create the noise generation model that differs from the number ofsamples used to calculate the past amplitude fluctuations σa.

When a mean of past amplitude fluctuations σa is used as the pastamplitude fluctuations σa, the acquiring unit 52 may calculate multiplepast amplitude fluctuations σa, and use the arithmetic means of the pastamplitude fluctuations σa as the past amplitude fluctuations σa. Here,the acquiring unit 52 may calculate the multiple past amplitudefluctuations σa using amplitude values obtained using the sliding doortechnique described above. The acquiring unit 52 calculates pastamplitude fluctuations σa P times, and calculates, as the past amplitudefluctuations σa, the arithmetic means, which is the sum of the P pastamplitude fluctuations σa divided by P.

The detecting unit 54 calculates the amplitude fluctuation ratio AR onthe basis of Equation (3) below from the past amplitude fluctuations σaand the amplitude fluctuations σb used to create the model. When theamplitude fluctuation ratios AR calculated by the detecting unit 54 arearranged in a time series, the waveforms can be those shown, forexample, in FIG. 8 through FIG. 10. In FIG. 8 through FIG. 10, thehorizontal axis represents the time, and the vertical axis representsthe amplitude fluctuation ratios AR of each modem 20. The waveforms ofthe amplitude fluctuation ratios AR shown in FIG. 8 through FIG. 10correspond, respectively, to the waveforms for the amplitudes shown inFIG. 5 through FIG. 7.

Equation 3

AR=σ _(b)/σ_(a)  (3)

The detecting unit 54 may calculate the amplitude fluctuation ratio ARseveral times for each modem 20. The detecting unit 54 also calculatesat least one amplitude fluctuation ratio AR on the basis of theamplitude fluctuation σb when noise is being generated. The detectingunit 54 associates the calculated amplitude fluctuation ratios AR withthe presence or absence of noise when the amplitude fluctuations σb weresampled, and outputs the association to the model creating unit 56 orthe storage unit 58.

The model creating unit 56 creates a noise generation model for a modem20 on the basis of the acquired amplitude fluctuation ratio AR (S24).For example, the model creating unit 56 creates a noise generating modemfor a modem 20 in a logistic regression technique using the presence orabsence of noise generated by the modem 20 as the objective variable andthe amplitude fluctuation ratio AR when the noise generation model isgenerated for the modem 20 as the description function. The objectivevariable is “1” when noise is generated during the sampling of anamplitude fluctuation σb while a noise generation model is beingcreated, and is “0” when noise is not generated. The value of theobjective variable is associated with the modem and with the amplitudefluctuation ratio AR on the basis of the past history, and stored in thestorage unit 58. More specifically, the model creating unit 56 creates anoise generation model able to calculate the noise generationprobability p of the modem 20 on the basis of Equation (4) below using alogistic regression technique. In Equation (4), β0 and β1 are constants.The model creating unit 56 calculates β0 and β1 using a known solutionto the logistic regression technique such as the maximum-likelihoodmethod. The model creating unit 56 outputs the created noise generationmodel to the detecting unit 54. The model creating unit 56 creates onenoise generation model shown in Equation (4) for all modems 20.

$\begin{matrix}{{Equation}\mspace{14mu} 4} & \; \\{p = \frac{1}{1 + ^{- {({{\beta \; 0} + {\beta \; 1*{AR}}})}}}} & (4)\end{matrix}$

p: Probability of noise generation in modemAR: Amplitude fluctuation rate

FIG. 11 is a flowchart of the process for detecting the noise generatinglocation in Step S12. The identifying device 50 executes the process fordetecting the location at which noise is being generated when noiseoccurs.

In the process performed to detect the location at which noise is beinggenerated, as shown in FIG. 11, the acquiring unit 52 first calculatesthe amplitude fluctuations of each modem 20 during the detection timeDT, which includes the time at which noise is generated (S30). Here, theacquiring unit 52 may calculate the amplitude fluctuations σb using thesame method as in Step S20. The acquiring unit 52 may set a detectiontime DT in Step S30 that differs from the detection time DT in Step S20.For example, the acquiring unit 52 may align the detection time DT inStep S30 with the time at which noise is generated. The acquiring unit52 outputs the amplitude fluctuations σb of each modem 20 acquiredduring the process performed to detect the location at which noise isbeing generated to the detecting unit 54 along with the identificationsignals of the modems 20.

Next, the detecting unit 54 calculates, as the amplitude fluctuationratio AR during the detection time, the ratio of the amplitudefluctuations σb of each modem 20 acquired by the acquiring unit 52during the detection time to the past amplitude fluctuations σa of eachmodem 20 stored in the storage unit 58 (S32).

Next, the detecting unit 54 detects the location at which the noise isbeing generated on the basis of the noise generation model and theamplitude fluctuation ratio AR, and outputs the detected location atwhich noise is being generated to a display device (S34). For example,the detecting unit 54 substitutes the amplitude fluctuation ratio AR inthe noise generation model shown in Equation (4) created by the modelcreating unit 56, and calculates the noise generating probability p ofeach modem 20. The detecting unit 54 detects the location at which noiseis being generated on the basis of the noise generating probability p.

For example, the detecting unit 54 detects the modem 20 with the highestnoise generating probability p as the location at which noise is beinggenerated. The detecting unit 54 may also detect one or more modems 20with a noise generating probability p exceeding a threshold value aslocations at which noise is being generated. The detecting unit 54 mayalso detect the location at which noise is being generated on the basisof the difference between the noise generating probability p of onemodem 20 and the noise generating probability p of other modems 20 witha high noise generating probability p. In this case, the detecting unit54 may detect all modems 20 with a noise generating probability pgreater than the noise generating probability p of a modem 20 serving asthe threshold value for the difference as locations at which noise isbeing generated because the difference exceeds a threshold value.

When performing another detection of the location at which noise isbeing generated, the detecting unit 54 may detect, as the location atwhich noise is being generated, a mid-level device such as a node 14, anamplifier 16 or a housing complex device 18. Here, locations at whichnoise is being generated may include noise generated by the mid-leveldevice itself or noise generated by a modem 20 connected to themid-level device downstream. In this case, the detecting unit 54 detectsa first non-event probability 1-pi, which is the probability that amodem 20 is not generating noise, for each of the modems 20 on the basisof the amplitude fluctuations. Here, pi is the noise-generatingprobability 20 of each modem 20 connected to the mid-level device whichwas calculated by the detecting unit 54 on the basis of Equation (4).The detecting unit 54 calculates the noise generating probability pΠ,which is the probability that signals transmitted by the mid-leveldevice contain noise, on the basis of a value obtained by multiplyingthe first non-event probability 1-pi of all modems 20 connected to eachof a plurality of mid-level devices on the basis of Equation (5) below.The detecting unit 54 calculates the noise generating probability pΠ ofeach mid-level device belonging to the same level. The noise generatingprobability pΠ is an example of a second event probability. Thedetecting unit 54 detects, as the mid-level device connected to thenoise-generating modem 20, the mid-level device with the highest noisegenerating probability pΠ among all of the mid-level devices belongingto the same level.

$\begin{matrix}{{Equation}\mspace{14mu} 5} & \; \\\begin{matrix}{{p\; \Pi} = {1 - {\prod\limits_{i = 1}^{M}\left( {1 - p_{i}} \right)}}} \\{= {1 - {\left( {1 - p_{1}} \right)\left( {1 - p_{2}} \right)\mspace{14mu} \ldots \mspace{14mu} \left( {1 - p_{M}} \right)}}}\end{matrix} & (5)\end{matrix}$

pΠ: Probability of noise generation by mid-level deviceM: Number of modems connected to mid-level device

For example, when a housing complex device 18 is to be detected as amid-level device at which noise is being generated, the noise generatingprobability p of each modem 20 connected to each housing complex device18 is substituted in Equation (5) to calculate the noise generatingprobability pΠ of the housing complex devices 18. The detecting unit 54detects, as the location at which noise is being generated, the housingcomplex device 18 with the highest calculated noise generatingprobability pΠ. This includes situations in which the noise is beinggenerated by the housing complex device 18 itself and situations inwhich noise is being generated by one or more of the modems 20 connectedto the housing complex device 18.

When performing yet another detection of the location at which noise isbeing generated, the detecting unit 54 may combine the two detectionmethods described above. For example, when the noise generatingprobability p of a modem 20 exceeds a threshold value, the detectingunit 54 detects more than one modem 20 as the location at which noise isbeing generated on the basis of the noise generating probabilities p.When the highest noise generating probability p does not exceed thethreshold value as described above, the detecting unit 54 calculates thenoise generating probabilities pΠ of the mid-level devices to detectwhether any of the mid-level devices is the location at which noise isbeing generated.

As mentioned above, the identifying device 50 acquires amplitudefluctuations σb from the amplitudes of transmission signals that can beeasily acquired by the acquiring unit 52, and the detecting unit 54detects the location at which noise is being generated on the basis ofthe amplitude fluctuations σb. In this way, the identifying device 50can detect a location at which noise is being generated using a simpleconfiguration and process. More specifically, the detecting unit 54 candetect a location at which noise is being generated more easily and moreprecisely using the amplitude fluctuation ratio AR of the amplitudefluctuation σb at the time of detection to a past amplitude fluctuationσa.

In the identifying device 50, the detecting unit 54 can detect the noisegenerating probability p of each modem 20 using a noise generation modelcreated by a model creating unit 56 using a logistic regressiontechnique. In this way, the detecting unit 54 can improve theprobability of detecting the location at which noise is being generateddown to the smallest unit, that is, down to the level of a modem.

The identifying device 50 can calculate the noise generating probabilitypΠ of mid-level devices (such as housing complex devices 18) on thebasis of the noise generating probabilities p calculated by thedetecting unit 54. By detecting a mid-level device as thenoise-generating device when the difference in noise generatingprobabilities p between modems 20, the detecting unit 54 can quickly andeasily isolate the location at which noise is being generated. Even whennoise is generated temporarily, the identifying device 50 can improvethe probability of detecting the location at which noise is beinggenerated.

When the identifying device 50 is calculating amplitude fluctuationratios AR, the detecting unit 54 can use the means of several pastamplitude fluctuations σa as the past amplitude fluctuation. In thiscase, the detecting unit 54 can calculate amplitude fluctuation ratiosAR using more appropriate past amplitude fluctuations σa in which theeffect of noise has been reduced, even when past amplitude fluctuationsσa during the generation of noise are included.

The connection relationships, the values such as the number ofconnections, and the functions can be changed in the configuration ofthe embodiment described above if necessary.

In the embodiment described above, it was assumed that signalstransmitted by the modems 20 could be received. However, if the power isoff and transmitted signals cannot be received from a modem 20, a noisegeneration model may be created and the location at which noise is beinggenerated may be detected on the basis of an amplitude fluctuationcalculated from past amplitudes received from the modem 20 or from themean of these amplitude fluctuations.

In the embodiment described above, a single noise generation model wasgenerated for a single network 10. However, noise generation models maybe generated for each node 14, amplifier 16, housing complex device 18,and modem 20.

In the embodiment described above, it was assumed that the modems 20were connected to terminals in the network 10. However, the embodimentdescribed above may also be applied to detect whether or not noise isbeing generated by a terminal that is not connected to a modem 20. Here,the terminal is simply treated as a first device.

In the embodiment described above, the model creating unit 56 creates anoise generation model using a logistic regression technique. However,the noise generation model may also be created using some other method.

In the embodiment described above, the identifying device 50 wasinstalled in a broadcast facility 12. However, an identifying device 50may be provided for each node 14, amplifier 16, and housing complexdevice 18. For example, when an identifying device 50 described above isprovided for each node 14, the nodes 14 serve as examples of upper-leveldevices.

FIG. 12 shows an example of a hardware configuration for a computer 1900in the present embodiment. The computer 1900 is an example of anidentifying device 50. The computer 1900 is equipped with a CPUperipheral portion having a CPU 2000, RAM 2020, graphics controller 2075and display device 2080 connected to each other by a host controller2082, an input/output portion having a communication interface 2030 anda hard disk drive 2040 connected to the host controller 2082 by aninput/output controller 2084, and a legacy input/output portion having aROM 2010, flexible disk drive 2050, and input/output chip 2070 connectedto the input/output controller 2084.

The host controller 2082 is connected to RAM 2020, a CPU 2000 accessingthe RAM 2020 at a high transfer rate, and a graphics controller 2075.The CPU 2000 is operated on the basis of a program stored in the ROM2010 and the RAM 2020, and controls the various units. The graphicscontroller 2075 acquires the image data generated in the frame buffer ofthe RAM 2020 by the CPU 2000 and other units, and displays this imagedata on the display device 2080. Alternatively, the graphics controller2075 can include a frame buffer for storing image data generated by theCPU 2000 and other units.

The input/output controller 2084 is connected to a host controller 2082,a communication interface 2030 serving as a relatively high-speedinput/output device, and a hard disk drive 2040. The communicationinterface 2030 communicates with the other devices via a network. Thehard disk drive 2040 stores programs, such as the display program usedby the CPU 2000 in the computer 1900, and data.

The input/output controller 2084 is connected to the ROM 2010, a memorydrive 2050, and the relatively low-speed input/output device of theinput/output chip 2070. The ROM 2010 stores the boot program executed bythe computer 1900 at startup and/or programs relying on hardware in thecomputer 1900. The flexible disk drive 2050 reads programs or data froma memory card 2090, and provides the programs and data to the hard diskdrive 2040 via the RAM 2020. The input/output chip 2070 connects theflexible disk drive 2050 to the input/output controller 2084, andvarious types of input/output devices are connected to the input/outputcontroller 2084 via a parallel port, serial port, keyboard port, ormouse port, etc.

A program provided to the hard disk drive 2040 via the RAM 2020 isstored on a recording medium such as a memory card 2090 or IC cardprovided by the user. A program is read from the recording medium,installed in the hard disk drive 2040 inside the computer 1900 via theRAM 2020, and executed by the CPU 2000.

Programs causing the computer 1900 to function as the identifying device50 include an acquiring module, detection module, and generating module.These programs or modules may work with the CPU 2000 and othercomponents to cause the computer 1900 to function as the acquiringmodule, detection module, and generating module.

The information processing steps written in these programs are specificmeans activated by reading the programs to the computer 1900 so that thesoftware cooperates with the various types of hardware resourcesdescribed above. These specific means function as the acquiring module,detection module and generating module. These specific means realizeoperations and the processing of information in the computer 1900 of thepresent embodiment to construct an identifying device 50 for thisintended purpose.

For example, when the computer 1900 communicates with an externaldevice, the CPU 2000 executes the communication program loaded in theRAM 2020, and instructs the communication interface 2030 in thecommunication processing on the basis of the processing contentdescribed in the communication program. The communication interface 2030is controlled by the CPU 2000, and reads the transmitted data stored inthe transmission buffer region of a memory device such as the RAM 2020,hard disk drive 2040 or memory card 2090, or writes reception datareceived from the network to a reception buffer region of the storagedevice. In this way, the communication interface 2030 transferstransmitted and received data to a storage device using the directmemory access (DMA) method. Alternatively, the CPU 2000 transferstransmitted and received data by reading data from the source storagedevice or communication interface 2030, and transfers and writes data tothe destination communication interface 2030 or storage device.

Also, the CPU 2000 writes all of the data or the necessary data to theRAM 2020 via, for example, a DMA transfer, from files or databasesstored in an external storage device such as a hard disk drive 2040 or amemory drive 2050 (memory card 2090), and performs various types ofprocessing on the data in the RAM 2020. The CPU 2000 then writes theprocessed data to an external storage device via, for example, a DMAtransfer. Because the RAM 2020 temporarily stores the contents of theexternal storage device during this process, the RAM 2020 and theexternal storage device are generally referred to in the presentembodiment as memory, a storage unit, or a storage device. The varioustypes of information in the programs, data, tables and databases of thepresent embodiment are stored in these memory devices, and are thetargets of information processing. The CPU 2000 can hold some of the RAM2020 in cache memory, and read and write data to the cache memory. Evenhere the cache memory performs some of the functions of the RAM 2020.Therefore, this division is excluded in the present embodiment. Cachememory is included in the RAM 2020, the memory, and/or the storagedevice.

The CPU 2000 also performs various types of processing on data read fromthe RAM 2020 including the operations, processing, conditiondetermination, and information retrieval and substitution described inthe present embodiment and indicated by a sequence of instructions inthe program, and writes the results to the RAM 2020. For example, whenperforming a condition determination, the CPU 2000 compares varioustypes of variables described in the present embodiment to othervariables or constants to determine whether or not conditions such asgreater than, less than, equal to or greater than, equal to or less thanor equal to have been satisfied. When a condition has been satisfied (ornot satisfied), the process branches to a different sequence ofinstructions or calls up a subroutine.

A program or module described above can be stored in a recording mediumof an external unit. Instead of a memory card 2090, the recording mediumcan be an optical recording medium such as a DVD or CD, amagneto-optical recording medium such as MO, a tape medium, or asemiconductor memory such as an IC card. The recording medium can alsobe a storage device such as a hard disk or RAM provided in a serversystem connected to a dedicated communication network or the internet,and the program can be provided to the computer 1900 via the network.

The present invention was explained using an embodiment, but thetechnical scope of the present invention is not limited to theembodiment described above. The possibility of many changes andimprovements to this embodiment should be apparent to those skilled inthe art. Embodiments including these changes and improvements are withinthe technical scope of the present invention, as should be clear fromthe description of the claims.

The order of execution for operations, steps and processes in thedevices, systems, programs and methods described in the claims,description and drawings were described using such terms as “previous”and “prior”. However, these operations, steps and processes can berealized in any order as long as the output of the previous process isused by the subsequent process. The operational flow in the claims,description and drawings were explained using terms such as “first” and“next” for the sake of convenience. However, the operational flow doesnot necessarily have to be executed in this order.

REFERENCE SIGNS LIST

-   -   10: Network    -   12: Broadcast facility    -   14: Node    -   16: Amplifier    -   18: Housing complex device    -   20: Modem    -   50: Identifying device    -   52: Acquiring unit    -   54: Detecting unit    -   56: Generating unit    -   58: Storage unit    -   60: Control unit    -   1900: Computer    -   2000: CPU    -   2010: ROM    -   2020: RAM    -   2030: Communication interface    -   2040: Hard disk drive    -   2050: Memory drive    -   2070: Input/output chip    -   2075: Graphics controller    -   2080: Display device    -   2082: Host controller    -   2084: Input/output controller    -   2090: Memory card

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for identifying alocation of noise in a network, the method comprising: acquiring, by anidentifying device having a central processing unit, amplitudefluctuations in signals transmitted from first devices via at least onesecond device to an upper-level device in a network, by the identifyingdevice, the network being a tree-like structure having the upper-leveldevice at a highest level of the network, one or more second devices atone or more middle levels of the network, and a plurality of firstdevices at a lowest level of the network; and detecting, by theidentifying device, a noise-generating device that is transmittingsignals containing noise on the basis of the acquired amplitudefluctuations in signals.
 2. The method of claim 1, further comprisingacquiring, by the identifying device, an identification signal from thefirst devices along with the signals transmitted from the first devicesand associating an amplitude fluctuation with respective first deviceson the basis of the identification signals from the first devices. 3.The method of claim 1, wherein: the one or more second devices includesat least two second devices at a same middle level, and the detecting ofa noise-generating device includes detecting whether thenoise-generating device is downstream from a particular one of thesecond devices.
 4. The method of claim 3, wherein the detecting of anoise-generating device includes: determining a non-event probabilityfor each of the first devices, the non-event probability being aprobability of an absence of noise being generated by a first device,determining an event probability for each of the second devices at asame level, the event probability being a probability of noise beingincluded in signals transmitted from a second device, wherein an eventprobability for a particular second device is determined as a product ofnon-event probabilities of all of the first devices connected to theparticular second device, and detecting a particular second device oftwo or more second devices at a same level as a noise-generating devicewhen the particular second device has a highest event probability of thetwo or more second devices at the same level.
 5. The method of claim 1,wherein the identifying device includes a storage device to store firstamplitude fluctuations of the first devices acquired at a first time,wherein: the detecting, by the identifying device, of a noise-generatingdevice includes detecting a particular first device as thenoise-generating device by comparing a first amplitude fluctuation ofthe particular first device to an amplitude fluctuation of theparticular first device acquired at a time of detection, the first timepreceding the time of detection.
 6. The method of claim 5, wherein thedetecting, by the identifying device, of a noise-generating deviceincludes detecting the particular first device as the noise-generatingdevice using an amplitude fluctuation ratio of the amplitude fluctuationof the particular first device at the time of detection to the firstamplitude fluctuation at the first time of the particular first device.7. The method of claim 6, further comprising generating a noisegeneration model by the identifying device, wherein: the detecting of anoise-generating device includes detecting a first device as thenoise-generating device using the noise generation model, and the noisegeneration model is generated using logistic regression, wherein apresence or absence of noise in a particular first device is anobjective variable and the ratio of the amplitude fluctuation of theparticular first device at the time of detection to the first amplitudefluctuation of the particular first device in the particular firstdevice is a description function.
 8. The method of claim 6, wherein thedetecting, by the identifying device, of a noise-generating device usesa mean of a plurality of first amplitude fluctuations of the particularfirst device as the first amplitude fluctuation of the particular firstdevice in the ratio of the amplitude fluctuation of the particular firstdevice at the time of detection to the first amplitude fluctuation ofthe particular first device.